

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_      =     logging.get_logger(__name__)

a_      =     {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='deberta-v2'
			def __init__(      self				:			List[str]   ,   a				:			int=12_8100   ,   a				:			str=1536   ,   a				:			List[Any]=24   ,   a				:			Union[str, Any]=24   ,   a				:			Union[str, Any]=6144   ,   a				:			Dict="gelu"   ,   a				:			Dict=0.1   ,   a				:			Dict=0.1   ,   a				:			Any=512   ,   a				:			Optional[int]=0   ,   a				:			Any=0.02   ,   a				:			int=1e-7   ,   a				:			Dict=False   ,   a				:			Optional[int]=-1   ,   a				:			int=0   ,   a				:			Union[str, Any]=True   ,   a				:			Dict=None   ,   a				:			List[Any]=0   ,   a				:			Tuple="gelu"   ,   **a				:			str   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Any					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Dict					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   int					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							initializer_range
				SCREAMING_SNAKE_CASE :   str					  =							relative_attention
				SCREAMING_SNAKE_CASE :   Dict					  =							max_relative_positions
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pad_token_id
				SCREAMING_SNAKE_CASE :   Tuple					  =							position_biased_input

				# Backwards compatibility
				if type(a						) == str:
					SCREAMING_SNAKE_CASE :   Any					  =							[x.strip() for x in pos_att_type.lower().split("|"						)]

				SCREAMING_SNAKE_CASE :   Dict					  =							pos_att_type
				SCREAMING_SNAKE_CASE :   Dict					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Any					  =							layer_norm_eps

				SCREAMING_SNAKE_CASE :   Any					  =							kwargs.get("pooler_hidden_size"   ,   a						)
				SCREAMING_SNAKE_CASE :   int					  =							pooler_dropout
				SCREAMING_SNAKE_CASE :   List[str]					  =							pooler_hidden_act






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							{0: "batch", 1: "sequence"}
				if self._config.type_vocab_size > 0:
					return OrderedDict(
					    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]						)
				else:
					return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]						)
			@property
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				return 12






			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional["TensorType"] = None   ,   a				:			int = 3   ,   a				:			int = 40   ,   a				:			int = 40   ,   a				:			"PreTrainedTokenizerBase" = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							super().generate_dummy_inputs(preprocessor=a   ,   framework=a						)
				if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
					del dummy_inputs["token_type_ids"]
				return dummy_inputs
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)